Report of the fit

Fit summary

Description: Test fit with the MAP 2022 parameterisation.
Minimiser: ceres
Random seed: 1234
Maximum values allowed for q_T / Q for Drell Yan dataset: 0.2
Maximum values allowed for q_T / Q for SIDIS dataset: min[ min[ 0.2 / z, 0.5 ] + 0.3 / (z*Q) ], 1 ]
Percentile cut: 5
Parameterisation: MAP22g52
Initial parameters fluctuations: False
Explicit formula:

f_{\rm NP}(x,\zeta, b_T)= \exp(S_{\rm NP}(\zeta, b_T))\frac{g_1(x) \exp( - g_1(x) \frac{b_T^2}{4}) + \lambda^2 g_{1B}^2(x) ( 1 - g_{1B}(x) \frac{b_T^2}{4}) \exp( - g_{1B}(x) \frac{b_T^2}{4}) + \lambda_2^2 g_{1C}(x) exp( - g_{1C}(x) \frac{b_T^2}{4}) }{ g_1(x) + \lambda^2 g_{1B}^2(x) + \lambda_2^2 g_{1C}(x) }D_{\rm NP}(z,\zeta, b_T)= \exp(S_{\rm NP}(\zeta, b_T))\frac{g_3(z) \exp( - g_3(z) \frac{b_T^2}{4z^2}) + \frac{\lambda_F}{z^2} g_{3B}^2(z) \big ( 1 - g_{3B}(z) \frac{b_T^2}{4z^2} \big ) \exp( - g_{3B}(z) \frac{b_T^2}{4z^2}) }{ g_3(z) + \frac{\lambda_F}{z^2} g_{3B}^2(z) }S_{\rm NP} = \exp\left[ - g_2^2 \frac{b_T^2}{4} \log \big (\frac{\zeta}{Q_0^2}\big ) \right]g_{1,1B,1C}(x) = N_{1,1B,1C} \frac{x^{\sigma_{1,2,3}}(1-x)^{\alpha^2_{1,2,3}}}{\hat{x}^{\sigma_{1,2,3}}(1-\hat{x})^{\alpha^2_{1,2,3}}}g_{3,3B}(z) = N_{3,3B} \frac{(z^{\beta_{1,2}}+\delta^2_{1,2})(1-z)^{\gamma^2_{1,2}}}{(\hat{z}^{\beta_{1,2}}+\delta^2_{1,2})(1-\hat{z})^{\gamma^2_{1,2}}}Q_0^2 = 1\;{\rm GeV}^2\hat{x} = 0.1\hat{z} = 0.5 t_0 prescription: False
## Theory summary

Collinear PDF set: MMHT2014nlo68cl member 0
Collinear FF set: DSS14_NLO_Pip member 0
Collinear FF set: DSS14_NLO_Pim member 0
Collinear FF set: DSS17_NLO_KaonPlus member 0
Collinear FF set: DSS17_NLO_KaonMinus member 0
b^* prescription: bstarmin
Perturbative order: NNLL
Reference value of the fine-structure constant: \alpha(Q = 91.1876\;{\rm GeV}) = 0.00776578395589 (running True)

Global statistical estimators

N_{rep} = 200
\chi_{0}^2 = 0.9206
\chi_{mean}^2 = 0.9102
\langle\chi^2\rangle \pm \sigma_{\chi^2} = 0.8899 \pm 0.0385
\langle E \rangle \pm \sigma_{E} = 1.8763 \pm 0.0655

Parameters

Fit properties

Table of \chi^2’s

TMDs in k_\perp or P_\perp space